1. Field of the Invention
The present invention relates to methods for characterizing petroleum fluids extracted from a hydrocarbon-bearing geological formation. The invention has application to reservoir architecture understanding, although it is not limited thereto.
2. Description of Related Art
Petroleum consists of a complex mixture of hydrocarbons of various molecular weights, plus other organic compounds. The exact molecular composition of petroleum varies widely from formation to formation. The proportion of hydrocarbons in the mixture is highly variable and ranges from as much as 97 percent by weight in the lighter oils to as little as 50 percent in the heavier oils and bitumens. The hydrocarbons in petroleum are mostly alkanes (linear or branched), cycloalkanes, aromatic hydrocarbons, or more complicated chemicals like asphaltenes. The other organic compounds in petroleum typically contain carbon dioxide (CO2), nitrogen, oxygen, and sulfur, and trace amounts of metals such as iron, nickel, copper, and vanadium.
Petroleum is usually characterized by saturates-aromatics-resins-asphaltenes (SARA) fractionation where asphaltenes are removed by precipitation with a paraffinic solvent and the deasphalted oil separated into saturates, aromatics, and resins by chromatographic separation.
The saturates include alkanes and cycloalkanes. The alkanes, also known as paraffins, are saturated hydrocarbons with straight or branched chains which contain only carbon and hydrogen and have the general formula CnH2n+2. They generally have from 5 to 40 carbon atoms per molecule, although trace amounts of shorter or longer molecules may be present in the mixture. The alkanes include methane (CH4), ethane (C2H6), propane (C3H8), i-butane (iC4H10), n-butane (nC4H10), i-pentane (iC5H12), n-pentane (nC5H12), hexane (C6H14), heptane (C7H16), octane (C8H18), nonane (C9H20), decane (C10H22), hendecane (C11H24)— also referred to as endecane or undecane, dodecane (C12H26), tridecane (C13H28), tetradecane (C14H30), pentadecane (C15H32) and hexadecane (C16H34). The cycloalkanes, also known as napthenes, are saturated hydrocarbons which have one or more carbon rings to which hydrogen atoms are attached according to the formula CnH2n. Cycloalkanes have similar properties to alkanes but have higher boiling points. The cycloalkanes include cyclopropane (C3H6), cyclobutane (C4H8), cyclopentane (C5H10), cyclohexane (C6H12), cycloheptane (C7H14), etc.
The aromatic hydrocarbons are unsaturated hydrocarbons which have one or more planar six carbon rings called benzene rings, to which hydrogen atoms are attached with the formula CnHn. They tend to burn with a sooty flame, and many have a sweet aroma. The aromatic hydrocarbons include benzene (C6H6) and derivatives of benzene, and polyaromatic hydrocarbons.
Resins are the most polar and aromatic species present in the deasphalted oil and, it has been suggested, contribute to the enhanced solubility of asphaltenes in crude oil by solvating the polar and aromatic portions of the asphaltenic molecules and aggregates.
Asphaltenes are insoluble in n-alkanes (such as n-pentane or n-heptane) and soluble in toluene. The C:H ratio is approximately 1:1.2, depending on the asphaltene source. Unlike most hydrocarbon constituents, asphaltenes typically contain a few percent of other atoms (called heteroatoms), such as sulfur, nitrogen, oxygen, vanadium, and nickel. Heavy oils and tar sands contain much higher proportions of asphaltenes than do medium-API oils or light oils. Condensates are virtually devoid of asphaltenes. As far as asphaltene structure is concerned, experts agree that some of the carbon and hydrogen atoms are bound in ring-like, aromatic groups, which also contain the heteroatoms. Alkane chains and cyclic alkanes contain the rest of the carbon and hydrogen atoms and are linked to the ring groups. Within this framework, asphaltenes exhibit a range of molecular weight and composition. Asphaltenes have been shown to have a distribution of molecular weight in the range of 300 to 1400 g/mol with an average of about 750 g/mol. This is compatible with a molecule containing seven or eight fused aromatic rings, and the range accommodates molecules with four to ten rings.
It is also known that asphaltene molecules aggregate to form nanoaggregates and clusters. The aggregation behavior depends on the solvent type. Laboratory studies have been conducted with asphaltene molecules dissolved in a solvent such as toluene. At extremely low concentrations (below 10−4 mass fraction), asphaltene molecules are dispersed as a true solution. At higher concentrations (on the order of 10−4 mass fraction), the asphaltene molecules stick together to form nanoaggregates. These nanoaggregates are dispersed in the fluid as a nanocolloid, meaning the nanometer-sized asphaltene particles are stably suspended in the continuous liquid phase solvent. At even higher concentrations (on the order of 5×10−3 mass fraction), the asphaltene nanoaggregates form clusters that remain stable as a colloid suspended in the liquid phase solvent. At higher concentrations (on the order of 5×10−2 mass fraction), the asphaltene clusters flocculate to form clumps which precipitate out of the toluene solvent. In crude oil, asphaltenes exhibit a similar aggregation behavior. However, at the higher concentrations (on the order of 5×10−2 mass fraction) that cause asphaltene clusters to flocculate in toluene, stability can continue such that the clusters form a viscoelastic network.
Computer-based modeling and simulation techniques have been developed for estimating the properties and/or behavior of petroleum fluid in a reservoir of interest. Typically, such techniques employ an equation of state (EOS) model that represents the phase behavior of the petroleum fluid in the reservoir. Once the EOS model is defined, it can be used to compute a wide array of properties of the petroleum fluid of the reservoir, such as: gas-oil ratio (GOR) or condensate-gas ratio (CGR), density of each phase, volumetric factors and compressibility, heat capacity and saturation pressure (bubble or dew point). Thus, the EOS model can be solved to obtain saturation pressure at a given temperature. Moreover, GOR, CGR, phase densities, and volumetric factors are byproducts of the EOS model. Transport properties, such as heat capacity or viscosity, can be derived from properties obtained from the EOS model, such as fluid composition. Furthermore, the EOS model can be extended with other reservoir evaluation techniques for compositional simulation of flow and production behavior of the petroleum fluid of the reservoir, as is well known in the art. For example, compositional simulations can be helpful in studying (1) depletion of a volatile oil or gas condensate reservoir where phase compositions and properties vary significantly with pressure below bubble or dew point pressures, (2) injection of non-equilibrium gas (dry or enriched) into a black oil reservoir to mobilize oil by vaporization into a more mobile gas phase or by condensation through an outright (single-contact) or dynamic (multiple-contact) miscibility, and (3) injection of CO2 into an oil reservoir to mobilize oil by miscible displacement and by oil viscosity reduction and oil swelling.
In the past few decades, fluid homogeneity in a hydrocarbon reservoir has been assumed. However, there is now a growing awareness that fluids are often heterogeneous or compartmentalized in the reservoir. A compartmentalized reservoir consists of two or more compartments that effectively are not in hydraulic communication. Two types of reservoir compartmentalization have been identified, namely vertical and lateral compartmentalization. Vertical compartmentalization usually occurs as a result of faulting or stratigraphic changes in the reservoir, while lateral compartmentalization results from barriers to horizontal flow.
Natural convection, biodegradation, adsorption, and external fluxes can also lead to non-equilibrium hydrocarbon distribution in a reservoir.
Reservoir compartmentalization can significantly hinder production and can make the difference between an economically-viable field and an economically-nonviable field. Techniques to aid an operator to accurately describe reservoir compartments and their distribution can increase understanding of such reservoirs and ultimately raise production and lower technical risk.
Conventionally, reservoir compartmentalization has been determined utilizing pressure-depth plots and pressure gradient analysis with traditional straight-line regression schemes. This process may, however, be misleading as fluid compositional changes and compartmentalization give distortions in the pressure gradients, which result in erroneous interpretations of fluid contacts or pressure seals. Additionally, pressure communication does not prove flow connectivity.
US Patent Application Publication 2009/0312997 provides a methodology for correlating composition data of live oil measured using a downhole fluid analysis tool with predicted composition data to determine whether asphaltenes are in an equilibrium distribution within the reservoir. The methodology treats asphaltenes within the framework of polymer solution theory (Flory-Huggins model as modified by Zuo, herein the Flory-Huggins-Zuo model). The methodology generates a family of curves that predicts asphaltene content as a function of depth. The curves can be viewed as a function of two parameters, the volume and solubility of the asphaltene particle. The curves can be fit to measured relative asphaltene content as derived from the downhole fluid analysis tool. There can be uncertainty in the fitting process as asphaltene volume can vary widely. In these instances, it can be difficult to assess the accuracy of the Flory-Huggins-Zuo model and the resulting determinations based thereon at any given time, and thus know whether or not there is a need to acquire and analyze more downhole samples in order to refine or tune the Flory-Huggins-Zuo model and the resulting determinations based thereon. Moreover, in the case that the distribution of asphaltenes diverges from the equilibrium distribution predicted by the model, it is difficult to distinguish whether the reservoir fluids are compartmentalized or in a state of thermodynamic non-equilibrium.